Sensors, such as cameras, depth sensors or infrared sensors, may be used to identify, track, or otherwise define an object moving in a space or region, such as a three-dimensional (3D) space or region. When using multiple sensors to track or define an object moving in a space or region, a problem may arise in calibrating the sensors to accurately identify, track, or define the moving object. For example, to acquire complete 3D image data for a large object with multiple sensors, the multiple sensors are typically placed far apart from each other, and with wide baselines in between. Such a layout can create challenges with respect to camera calibration due to varying or limited overlaps between the multiple sensor or camera views.
A conventional method used to calibrate multiple sensors can include a pairwise sensor calibration that involves aligning each sensor or camera one by one with respect to every other sensor. Such a process typically involves many permutations of pairwise calibration between each of the sets of sensors, and, as result is often time consuming and incurs high setup and maintenance costs. For example, the multiple sensors are typically installed at different locations and capture image data at varying angles, distances, and positions, all of which can make calibration of the multiple sensors, and the image data they capture, difficult, time consuming, and/or altogether inefficient when used for the purpose of identifying, tracking, or otherwise defining an object moving in a space or region.